Adaptive Network Modeling of Social Distancing Interventions

TL;DR

This paper develops a network-based model to analyze social distancing interventions during COVID-19, revealing that intervention timing and severity are more influential than speed of implementation.

Contribution

It introduces a novel network model with dynamic link activation/deletion to realistically simulate social distancing effects on disease spread.

Findings

Intervention timing significantly impacts disease control.

Severity of social distancing measures influences outbreak dynamics.

Model exhibits rich qualitative behaviors with few parameters.

Abstract

The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.